Spotlight: Synergizing Seed Exploration and Spot GPUs for DiT RL Post-Training
Reinforcement learning (RL) post-training of Diffusion Transformers (DiTs) is prohibitively expensive, requiring thousands of high-end GPUs. Existing works explore two directions to reduce cost: seed exploration improves training convergence by selecting high-contrast samples, yet adds compute to the critical path; spot GPUs offer 69--77\% lower cost, yet sit idle during training because DiT rollouts finish nearly simultaneously, which prevents LLM-style pipelining of rollout with training. Spot preemptions further break Sequence Parallelism (SP) groups, fragmenting GPU topology. We present Spotlight, the first system that harvests spot GPUs for DiT RL post-training. Spotlight rests on tw...
| Authors | Ruiqi Lai, Dakai An, Wei Gao et al. |
|---|---|
| Primary category | cs.DC |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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